A Review of Recombinase-Aided Amplification (RAA) in Veterinary Medicine

Rapid and accurate pathogen detection is crucial to mitigating the significant economic impacts of infectious animal diseases. Recombinase-aided amplification (RAA) has emerged as a highly promising isothermal diagnostic tool, characterized by its rapid turnaround time, robust amplification efficiency, and single-copy analytical sensitivity. In veterinary diagnostics, RAA has evolved from single-pathogen proof-of-concept assays to become a promising technology for point-of-care scenarios. This review critically evaluates the integration of RAA with real-time fluorescence, CRISPR-Cas systems, lateral flow dipsticks (LFDs), and suspension microarray technologies, highlighting their impact on assay sensitivity and point-of-care (POC) applicability. Despite recent advancements, including encouraging progress in multiplexing and expanded applications in companion animal medicine, inherent challenges such as matrix effects in crude clinical samples, aerosol contamination risks, and commercialization barriers remain. By benchmarking RAA against traditional molecular methods and structurally similar technologies like recombinase polymerase amplification (RPA), this review outlines future trajectories for optimizing RAA platforms, aiming to maximize their utility in routine veterinary surveillance and epidemiological control.

Authors

Institutions

Publication Details

Journal
Microorganisms
Published
2026-10-14
DOI
https://doi.org/10.3390/microorganisms14102352
Primary Topic
Biosensors and Analytical Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Review of Recombinase-Aided Amplification (RAA) in Veterinary Medicine

Jie Gao, Hua Zheng, Hao Mu, Yongqiang Miao et al.
Microorganisms
Biosensors and Analytical Detection
article

A Review of Recombinase-Aided Amplification (RAA) in Veterinary Medicine

Jie Gao, Hua Zheng, Hao Mu, Yongqiang Miao, Qianlin Chen, Shaomei Li, Yuandi Yu, Liu Yang, Yifan Zhang, Jie Zhou, Mingwen Li, Chunhua Huang
article en

Abstract

Rapid and accurate pathogen detection is crucial to mitigating the significant economic impacts of infectious animal diseases. Recombinase-aided amplification (RAA) has emerged as a highly promising isothermal diagnostic tool, characterized by its rapid turnaround time, robust amplification efficiency, and single-copy analytical sensitivity. In veterinary diagnostics, RAA has evolved from single-pathogen proof-of-concept assays to become a promising technology for point-of-care scenarios. This review critically evaluates the integration of RAA with real-time fluorescence, CRISPR-Cas systems, lateral flow dipsticks (LFDs), and suspension microarray technologies, highlighting their impact on assay sensitivity and point-of-care (POC) applicability. Despite recent advancements, including encouraging progress in multiplexing and expanded applications in companion animal medicine, inherent challenges such as matrix effects in crude clinical samples, aerosol contamination risks, and commercialization barriers remain. By benchmarking RAA against traditional molecular methods and structurally similar technologies like recombinase polymerase amplification (RPA), this review outlines future trajectories for optimizing RAA platforms, aiming to maximize their utility in routine veterinary surveillance and epidemiological control.

MicroorganismsVol. 14(10)
Chongqing Academy of Animal Science (CN), Ministry of Agriculture and Rural Affairs (CN)
Openalex Percentile: Top 24%
Biosensors and Analytical Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.